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from cmath import cos
from inspect import getargs
import logging
import os
import random
from datetime import datetime
import bisect
import copy
from sched import scheduler
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch import optim
from torch.cuda.amp import GradScaler
import faulthandler
import pathlib
import argparse
import time
try:
import wandb
except ImportError:
wandb = None
try:
import torch.utils.tensorboard as tensorboard
except ImportError:
tensorboard = None
try:
import horovod.torch as hvd
except ImportError:
hvd = None
from open_clip import create_model_and_transforms, trace_model, create_model
from training.data import get_data
from training.params import parse_args
from training.distributed import is_master, init_distributed_device, world_info_from_env
from training.logger import setup_logging
from training.scheduler import cosine_lr
from training.lp_train import train_one_epoch, evaluate
from open_clip.utils import get_tar_path_from_dataset_name, dataset_split, get_optimizer
from open_clip.utils import load_p, load_class_label
from open_clip.linear_probe import LinearProbe
def maintain_ckpts(args, startidx, all_idx_len):
for i in reversed(range(startidx, all_idx_len)):
if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")):
os.rename(
os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"),
)
if os.path.exists(
os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")
):
os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt"))
return
def update_top_k_performance(
new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True
):
"""
Record the top-k performance of the current epoch.
current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
"""
if isinstance(new_metrics_inputs, (list, tuple)):
new_metrics_inputs = np.mean(new_metrics_inputs)
return update_top_k_performance(
new_metrics_inputs,
current_top_k_ckpt_metrics,
args=args,
ckpt=ckpt,
bignumbetter=bignumbetter,
)
elif isinstance(new_metrics_inputs, dict):
new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
return update_top_k_performance(
new_metrics_inputs,
current_top_k_ckpt_metrics,
args=args,
ckpt=ckpt,
bignumbetter=bignumbetter,
)
elif isinstance(new_metrics_inputs, (float, int)):
update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
sorted_values = sorted(
current_top_k_ckpt_metrics.values(), reverse=bignumbetter
)
sorted_values_ = copy.deepcopy(sorted_values)
sorted_values.append(new_metrics_inputs)
sorted_values = sorted(sorted_values, reverse=bignumbetter)
sorted_values = sorted_values[:-1]
if sorted_values == sorted_values_:
return current_top_k_ckpt_metrics, new_metrics_inputs
else:
for i in range(len(sorted_keys)):
if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
update_flag[sorted_keys[i]] = True
for i in range(len(update_flag)):
if update_flag[i]:
maintain_ckpts(args, i, len(sorted_keys))
torch.save(
ckpt,
os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
)
break
return current_top_k_ckpt_metrics, new_metrics_inputs
# def updateifNone(a, b):
# a = b if None else a
# return a
def is_pretrained_params(n):
return (
n.startswith("clap_model.transformer")
or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
or n.startswith("clap_model.token_embedding")
or n.startswith("clap_model.ln_final")
or n.startswith("clap_model.logit_scale_t")
)
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
def config_lp_optimizer(model, data, args):
# set wd-related params to 0 if use adam optimizer
if args.optimizer == "adam":
args.wd = 0
args.wd_pretrained = 0
args.wd_new = 0
in_clap = lambda n, p: n.startswith("clap_model")
named_parameters = list(model.named_parameters())
optimizer = {}
scheduler = {}
# freeze text encoder
text_freeze_parameters = [
p
for n, p in named_parameters
if n.startswith("clap_model.transformer")
or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
or n.startswith("clap_model.token_embedding")
or n.startswith("clap_model.ln_final")
]
if args.freeze_text:
logging.info("Freeze Text!!!!")
for k in text_freeze_parameters:
k.requires_grad = False
if not args.lp_freeze:
exclude = (
lambda n, p: p.ndim < 2
or "bn" in n
or "ln" in n
or "bias" in n
or "logit_scale" in n
)
include = lambda n, p: not exclude(n, p)
# (yusong): we do not split the learning rate anymore
# p for n, p in named_parameters if in_clap(n,p) and exclude(n, p) and p.requires_grad
gain_or_bias_params = [
p for n, p in named_parameters if exclude(n, p) and p.requires_grad
]
# rest_params = [p for n, p in named_parameters if in_clap(n,p) and include(n, p) and p.requires_grad]
rest_params = [
p for n, p in named_parameters if include(n, p) and p.requires_grad
]
if args.train_data is None:
optimizer = None
scheduler = None
else:
total_steps = data["train"].dataloader.num_batches * args.epochs
if args.split_opt:
for x in ["lr", "beta1", "beta2", "eps", "wd"]:
for y in ["_new", "_pretrained"]:
if getattr(args, x + y) is None:
setattr(args, x + y, getattr(args, x))
gain_or_bias_pretrained_params = [
p
for n, p in named_parameters
if (exclude(n, p) and p.requires_grad) and is_pretrained_params(n)
]
rest_pretrained_params = [
p
for n, p in named_parameters
if (include(n, p) and p.requires_grad) and is_pretrained_params(n)
]
gain_or_bias_new_params = [
p
for n, p in named_parameters
if (exclude(n, p) and p.requires_grad)
and (not is_pretrained_params(n))
]
rest_new_params = [
p
for n, p in named_parameters
if (include(n, p) and p.requires_grad)
and (not is_pretrained_params(n))
]
pretrained_params_optimizer = get_optimizer(
[
{"params": gain_or_bias_pretrained_params, "weight_decay": 0.0},
{
"params": rest_pretrained_params,
"weight_decay": args.wd_pretrained,
},
],
lr=args.lr_pretrained,
betas=(args.beta1_pretrained, args.beta2_pretrained),
eps=args.eps_pretrained,
momentum=args.momentum_pretrained,
optimizer_name=args.optimizer,
)
pretrained_params_scheduler = cosine_lr(
pretrained_params_optimizer,
args.lr_pretrained,
args.warmup,
total_steps,
)
new_params_optimizer = get_optimizer(
[
{"params": gain_or_bias_new_params, "weight_decay": 0.0},
{"params": rest_new_params, "weight_decay": args.wd_new},
],
lr=args.lr_new,
betas=(args.beta1_new, args.beta2_new),
eps=args.eps_new,
momentum=args.momentum_new,
optimizer_name=args.optimizer,
)
new_params_scheduler = cosine_lr(
new_params_optimizer, args.lr_new, args.warmup, total_steps
)
optimizer["text"] = pretrained_params_optimizer
optimizer["audio"] = new_params_optimizer
scheduler["text"] = pretrained_params_scheduler
scheduler["audio"] = new_params_scheduler
if args.horovod:
pretrained_params_optimizer = hvd.DistributedOptimizer(
pretrained_params_optimizer,
named_parameters=model.named_parameters(),
)
new_params_optimizer = hvd.DistributedOptimizer(
new_params_optimizer, named_parameters=model.named_parameters()
)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(
pretrained_params_optimizer, root_rank=0
)
hvd.broadcast_optimizer_state(new_params_optimizer, root_rank=0)
else:
optimizer["clap"] = get_optimizer(
[
{"params": gain_or_bias_params, "weight_decay": 0.0},
{"params": rest_params, "weight_decay": args.wd},
],
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.eps,
momentum=args.momentum,
optimizer_name=args.optimizer,
)
scheduler["clap"] = cosine_lr(
optimizer["clap"], args.lr, args.warmup, total_steps
)
if args.horovod:
optimizer["clap"] = hvd.DistributedOptimizer(
optimizer["clap"], named_parameters=model.named_parameters()
)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer["clap"], root_rank=0)
# linear probe optimizer
else:
lp_params = [
p for n, p in named_parameters if (not in_clap(n, p)) and p.requires_grad
]
lp_optim = get_optimizer(
lp_params,
lr=args.lp_lr,
betas=(args.beta1, args.beta2),
eps=args.eps,
momentum=0.9,
optimizer_name=args.optimizer,
)
optimizer["lp"] = lp_optim
return optimizer, scheduler, text_freeze_parameters
def main():
args = parse_args()
time.sleep(args.sleep)
# sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
args.amodel = args.amodel.replace("/", "-")
# download sizes.json file
# (yusong): the below two lines are for debug
# print("setting up faulthandler")
# faulthandler.register(10)
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
args.class_index_dict = load_class_label(args.class_label_path)
# get the name of the experiments
if args.name is None:
args.name = "-".join(
[
datetime.now().strftime("%Y_%m_%d-%H_%M_%S"),
f"linear_probe" f"model_{args.amodel}",
f"lr_{args.lr}",
f"b_{args.batch_size}",
f"j_{args.workers}",
f"p_{args.precision}",
]
)
# discover initial world args early so we can log properly
args.distributed = False
args.local_rank, args.rank, args.world_size = world_info_from_env()
if args.remotedata and is_master(args):
for dataset_name in args.datasetnames:
for split in dataset_split[dataset_name]:
if not os.path.exists(f"./json_files/{dataset_name}/{split}"):
os.makedirs(f"./json_files/{dataset_name}/{split}")
os.system(
f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json"
)
args.log_path = None
if is_master(args, local=args.log_local):
log_base_path = os.path.join(args.logs, args.name)
os.makedirs(log_base_path, exist_ok=True)
log_filename = f"out-{args.rank}" if args.log_local else "out.log"
args.log_path = os.path.join(log_base_path, log_filename)
# avoid log dir in same name:
postfix = 0
while os.path.exists(args.log_path):
postfix += 1
log_base_path_new = log_base_path + "-" + str(postfix)
os.makedirs(log_base_path_new, exist_ok=True)
log_filename = f"out-{args.rank}" if args.log_local else "out.log"
args.log_path = os.path.join(log_base_path_new, log_filename)
# print(
# "Error. Experiment already exists. Use --name {} to specify a new experiment."
# )
# return -1
# Set logger
args.log_level = logging.DEBUG if args.debug else logging.INFO
setup_logging(args.log_path, args.log_level)
# fully initialize distributed device environment
device = init_distributed_device(args)
args.wandb = "wandb" in args.report_to or "all" in args.report_to
args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to
if is_master(args):
args.tensorboard_path = (
os.path.join(args.logs, args.name, "tensorboard")
if args.tensorboard
else ""
)
args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints")
for dirname in [args.tensorboard_path, args.checkpoint_path]:
if dirname:
os.makedirs(dirname, exist_ok=True)
else:
args.tensorboard_path = ""
args.checkpoint_path = ""
if args.copy_codebase:
copy_codebase(args)
assert args.precision in ["amp", "fp16", "fp32"]
if args.precision == "fp16":
logging.warning(
"It is recommended to use AMP mixed-precision instead of FP16. "
"FP16 support needs further verification and tuning, especially for train."
)
if args.horovod:
logging.info(
f"Running in horovod mode with multiple processes / nodes. Device: {args.device}."
f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
)
elif args.distributed:
logging.info(
f"Running in distributed mode with multiple processes. Device: {args.device}."
f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
)
else:
logging.info(f"Running with a single process. Device {args.device}.")
logging.info(f"openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}")
# Create CLAP model
clap_model, clap_model_cfg = create_model(
args.amodel,
args.tmodel,
args.pretrained,
precision=args.precision,
device=device,
jit=args.torchscript,
force_quick_gelu=args.force_quick_gelu,
openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir),
skip_params=False,
pretrained_audio=args.pretrained_audio,
pretrained_text=args.pretrained_text,
enable_fusion=args.enable_fusion,
fusion_type=args.fusion_type,
)
args.lp_out_ch = len(list(args.class_index_dict.keys()))
# Linear Probe
logging.info(f"linear probe using mlp: {args.lp_mlp}")
logging.info(f"linear probe using freeze: {args.lp_freeze}")
logging.info(f"linear probe act layer: {args.lp_act}")
logging.info(f"linear probe out ch: {args.lp_out_ch}")
logging.info(f"linear probe learning rate (if applicable): {args.lp_lr}")
logging.info(f"linear probe loss func: {args.lp_loss}")
logging.info(f"linear probe lp_metrics: {args.lp_metrics}")
model = LinearProbe(
clap_model,
mlp=args.lp_mlp,
freeze=args.lp_freeze,
in_ch=512,
out_ch=args.lp_out_ch,
act=args.lp_act,
) # in_ch is fixed (i.e., 512)
model = model.to(device)
if args.horovod:
with torch.no_grad():
for param in model.parameters():
param.set_(param.contiguous())
if args.trace:
model = trace_model(model, batch_size=args.batch_size, device=device)
if is_master(args):
logging.info("Linear Probe CLAP Model:")
logging.info(f"{str(clap_model)}")
logging.info("Params:")
params_file = os.path.join(args.logs, args.name, "params.txt")
with open(params_file, "w") as f:
for name in sorted(vars(args)):
val = getattr(args, name)
logging.info(f" {name}: {val}")
f.write(f"{name}: {val}\n")
if args.distributed and not args.horovod:
if args.use_bn_sync:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
ddp_args = {}
if args.ddp_static_graph:
# this doesn't exist in older PyTorch, arg only added if enabled
ddp_args["static_graph"] = True
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[device], find_unused_parameters=True, **ddp_args
)
data = get_data(args, clap_model_cfg)
assert len(data), "At least one train or eval dataset must be specified."
if args.trace:
assert "train" not in data, "Cannot train with traced model"
optimizer, scheduler, text_freeze_parameters = config_lp_optimizer(
model, data, args
)
scaler = GradScaler() if args.precision == "amp" else None
# optionally resume from a checkpoint
start_epoch = 0
if args.resume is not None:
if os.path.isfile(args.resume):
checkpoint = torch.load(args.resume, map_location=device)
if "epoch" in checkpoint:
# resuming a train checkpoint w/ epoch and optimizer state
start_epoch = checkpoint["epoch"]
sd = checkpoint["state_dict"]
if not args.distributed and next(iter(sd.items()))[0].startswith(
"module"
):
sd = {k[len("module.") :]: v for k, v in sd.items()}
model.load_state_dict(sd)
if args.split_opt:
if optimizer is not None:
for k, o_ in optimizer.items():
o_.load_state_dict(checkpoint[k + "_" + "optimizer"])
if optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer"])
if scaler is not None and "scaler" in checkpoint:
scaler.load_state_dict(checkpoint["scaler"])
logging.info(
f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})"
)
else:
# loading a bare (model only) checkpoint for fine-tune or evaluation
model.load_state_dict(checkpoint)
logging.info(
f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})"
)
if args.freeze_text:
print("Freeze Text!!!!")
for k in text_freeze_parameters:
k.requires_grad = False
else:
logging.info("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
cudnn.deterministic = False
# determine if this worker should save logs and checkpoints. only do so if it is rank == 0
args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args)
writer = None
if args.save_logs and args.tensorboard:
assert tensorboard is not None, "Please install tensorboard."
writer = tensorboard.SummaryWriter(args.tensorboard_path)
if args.wandb and is_master(args):
assert wandb is not None, "Please install wandb."
logging.debug("Starting wandb.")
args.train_sz = data["train"].dataloader.num_samples
if args.val_data is not None:
args.val_sz = data["val"].dataloader.num_samples
# you will have to configure this for your project!
wandb.init(
project="clap",
notes=args.wandb_notes,
name=args.wandb_notes,
tags=[],
config=vars(args),
)
if args.debug:
wandb.watch(model, log="all")
wandb.save(params_file)
logging.debug("Finished loading wandb.")
if "train" not in data:
evaluate(model, data, start_epoch, args, writer)
return
elif start_epoch == 0 and "val" in data and not args.no_eval:
evaluate(model, data, 0, args, writer)
if args.save_top_performance:
current_top_k_ckpt_metrics = {
i: 0 for i in range(args.save_top_performance)
} # initialize the top-k metric for ckpts to 0
for epoch in range(start_epoch, args.epochs):
# freeze the text param after (include) args.freeze_text_after, this is -1 by default
if epoch == args.freeze_text_after:
print("Text pretrained parameters are freezed since this epoch.")
for k in text_freeze_parameters:
k.requires_grad = False
if is_master(args):
logging.info(f"Start epoch {epoch}")
train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer)
completed_epoch = epoch + 1
if (
any(v in data for v in ("val", "imagenet-val", "imagenet-v2"))
and not args.no_eval
):
metrics = evaluate(model, data, completed_epoch, args, writer)
if args.save_top_performance:
top_k_dataset = args.top_k_checkpoint_select_dataset
top_k_metric = args.top_k_checkpoint_select_metric
filtered_metrics = [
v
for k, v in metrics.items()
if top_k_metric in k and top_k_dataset in k
] # check all R@10 metrics (all dataset) and use it to update the ckpt
# Saving checkpoints.
if args.save_logs:
opt_dict = {
k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items()
}
checkpoint_dict = {
"epoch": completed_epoch,
"name": args.name,
"state_dict": model.state_dict(),
}
checkpoint_dict.update(opt_dict)
if scaler is not None:
checkpoint_dict["scaler"] = scaler.state_dict()
if completed_epoch == args.epochs or (
args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
):
torch.save(
checkpoint_dict,
os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
)
if args.save_most_recent:
torch.save(
checkpoint_dict,
os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
)
if args.save_top_performance and not args.no_eval:
update_top_k_performance(
filtered_metrics,
current_top_k_ckpt_metrics,
args,
checkpoint_dict,
bignumbetter=True,
)
if args.wandb and is_master(args):
wandb.finish()
def copy_codebase(args):
from shutil import copytree, ignore_patterns
new_code_path = os.path.join(args.logs, args.name, "code")
if os.path.exists(new_code_path):
print(
f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
)
return -1
print(f"Copying codebase to {new_code_path}")
current_code_path = os.path.realpath(__file__)
for _ in range(3):
current_code_path = os.path.dirname(current_code_path)
copytree(
current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb")
)
print("Done copying code.")
return 1
if __name__ == "__main__":
main()
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